(1) Citypersons Dataset. Table REF  reports the results compared to state-of-the-arts on Citypersons. First of all, our algorithms has significant performance improvement under various fairness settings. For example, when leveraging ResNet-50, ours achieves the highest accuracy with an improvement of 0.9% MR\(^{-2}\)  from the closet competitor CSP [1]} on R\(_{50}\)  and 8.8% MR\(^{-2}\)  on R\(_{75}\) . Furthermore, it is worth noting that from Table REF , ours has also demonstrated the self-contained ability to handle occlusion issues in crowded scenes. Especially on Heavy occlusion subset, ours reports the brand-new state-of-the-arts of 42.1% MR\(^{-2}\) . This is probably because harder samples are mined with the introduced directional assignment metric, and thus training a more discriminant predictor. And we mainly improve the training matching process, there is no additional modification to the network structure. Therefore, there is no extra computation consumption during testing, and our inference time is comparable to others.
